Abstract
This paper scrutinizes the effect of Twitter-based sentiment on US sectoral returns using data from between 21 June 2010 and 13 April 2020. We apply causality in quantiles as a non-parametric measure, followed by a rolling window wavelet correlation. The former measures the manifestation of causality directed from Twitter-based sentiment towards US sectoral returns, whereas the latter measures the correlation of returns across decomposed series that correspond to different time horizons. Our results highlight symmetric changes in US sectoral returns that vary across different sectors. The healthcare, communications, materials, consumer discretionary, energy, staples, and information technology sectors are more sensitive to changes in Twitter-based sentiment across all quantiles. Our findings from the rolling window wavelet correlation point to low correlation values for all decomposed series (i.e., long-, medium-, and short-run). Our findings have value for investors in the US sectoral market because they may be helpful for constructing and rebalancing portfolios based on varying levels of correlation across different quantile distributions and investment periods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The North American Journal of Economics and Finance
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
